State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-critical domains demands reliable predictive performance and uncertainty quantification. While Bayesian neural networks provide a conceptually simple approach to serve those requirements, they suffer from the high dimensionality of the parameter space. Parameter-efficient fine-tuning (PEFT) methods, in particular low-rank adaptations (LoRA), have emerged as a popular strategy for adapting large-scale models to down-stream tasks by performing parameter inference on lower-dimensional subspaces. In this work, we investigate the suitability of PEFT methods for subspace Bayesian inference in large-scale Transformer-based vision models. We show that, indeed, combining BitFit, DiffFit, LoRA, and CoLoRA, a novel LoRA-inspired PEFT method, with Bayesian inference enables more robust and reliable predictive performance in MDE.

本研究解决了在计算机视觉中单目深度估计面临的模型可靠性和不确定性量化问题。通过将参数高效微调方法与贝叶斯推断相结合，提出了一种新颖的方法，以实现大型Transformer模型的更稳健的预测性能。研究结果表明，这种结合能够在安全关键领域提供更加可靠的性能表现。

用于不确定性意识深度估计的参数高效贝叶斯神经网络